{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2d5b3550",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af3cc69d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2f3aaf45",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc9db8fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b9071eb7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'TargetDetection_pb2'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Input \u001b[1;32mIn [6]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mTargetDetection_pb2\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'TargetDetection_pb2'"
     ]
    }
   ],
   "source": [
    "import TargetDetection_pb2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5e9cb392",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "71b17334",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>DATA_DATE</th>\n",
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       "      <td>2015-01-01</td>\n",
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       "      <td>2.58</td>\n",
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       "      <td>5.63</td>\n",
       "      <td>0.23</td>\n",
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       "      <td>1.87</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2.50</td>\n",
       "      <td>0.65</td>\n",
       "      <td>6.65</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.57</td>\n",
       "      <td>13.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>3.22</td>\n",
       "      <td>3.77</td>\n",
       "      <td>0.22</td>\n",
       "      <td>3.29</td>\n",
       "      <td>1.66</td>\n",
       "      <td>1.29</td>\n",
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       "      <th>2</th>\n",
       "      <td>2015-01-03</td>\n",
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       "      <td>1.14</td>\n",
       "      <td>7.76</td>\n",
       "      <td>0.65</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.56</td>\n",
       "      <td>13.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>2.52</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.22</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.49</td>\n",
       "      <td>4.72</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>4.17</td>\n",
       "      <td>1.04</td>\n",
       "      <td>4.02</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.56</td>\n",
       "      <td>11.51</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>3.65</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.82</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.27</td>\n",
       "      <td>3.63</td>\n",
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       "      <th>4</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>4.89</td>\n",
       "      <td>1.33</td>\n",
       "      <td>4.68</td>\n",
       "      <td>0.71</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2.52</td>\n",
       "      <td>0.60</td>\n",
       "      <td>10.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.81</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.52</td>\n",
       "      <td>5.18</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.55</td>\n",
       "      <td>3.38</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>741</th>\n",
       "      <td>2017-02-03</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>2.23</td>\n",
       "      <td>3.07</td>\n",
       "      <td>4.23</td>\n",
       "      <td>1.11</td>\n",
       "      <td>3.69</td>\n",
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       "      <td>3.20</td>\n",
       "      <td>3.66</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.76</td>\n",
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       "      <th>742</th>\n",
       "      <td>2017-02-04</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.69</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.54</td>\n",
       "      <td>1.65</td>\n",
       "      <td>3.82</td>\n",
       "      <td>...</td>\n",
       "      <td>3.76</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>2017-02-05</td>\n",
       "      <td>2.77</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.49</td>\n",
       "      <td>2.27</td>\n",
       "      <td>4.32</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.37</td>\n",
       "      <td>2.85</td>\n",
       "      <td>...</td>\n",
       "      <td>3.82</td>\n",
       "      <td>2.66</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>2017-02-06</td>\n",
       "      <td>3.81</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.86</td>\n",
       "      <td>2.52</td>\n",
       "      <td>3.35</td>\n",
       "      <td>4.53</td>\n",
       "      <td>1.27</td>\n",
       "      <td>3.65</td>\n",
       "      <td>...</td>\n",
       "      <td>3.03</td>\n",
       "      <td>4.89</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.26</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>2017-02-07</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.08</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3.32</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1.49</td>\n",
       "      <td>4.06</td>\n",
       "      <td>...</td>\n",
       "      <td>3.79</td>\n",
       "      <td>2.53</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.26</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>746 rows × 201 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      DATA_DATE     1     2     3     4     5     6     7      8     9  ...  \\\n",
       "0    2015-01-01  6.68  1.22  7.35  0.62  2.58  2.72  0.55  10.98  0.00  ...   \n",
       "1    2015-01-02  2.50  0.65  6.65  1.92  2.60  2.34  0.57  13.02  0.00  ...   \n",
       "2    2015-01-03  5.20  1.14  7.76  0.65  2.36  2.79  0.56  13.86  0.00  ...   \n",
       "3    2015-01-04  4.17  1.04  4.02  1.30  1.83  2.61  0.56  11.51  0.00  ...   \n",
       "4    2015-01-05  4.89  1.33  4.68  0.71  2.05  2.52  0.60  10.67  0.00  ...   \n",
       "..          ...   ...   ...   ...   ...   ...   ...   ...    ...   ...  ...   \n",
       "741  2017-02-03  3.18  1.99  0.00  0.76  2.23  3.07  4.23   1.11  3.69  ...   \n",
       "742  2017-02-04  3.29  0.96  0.00  0.75  2.69  3.18  3.54   1.65  3.82  ...   \n",
       "743  2017-02-05  2.77  1.36  0.00  1.49  2.27  4.32  2.10   1.37  2.85  ...   \n",
       "744  2017-02-06  3.81  1.33  0.00  0.86  2.52  3.35  4.53   1.27  3.65  ...   \n",
       "745  2017-02-07  3.62  1.40  0.00  1.08  2.10  3.32  2.82   1.49  4.06  ...   \n",
       "\n",
       "      191   192   193   194   195   196   197   198   199  200  \n",
       "0    2.26  5.63  0.23  2.34  1.78  1.87  0.00  4.59  4.88  0.0  \n",
       "1    3.22  3.77  0.22  3.29  1.66  1.29  0.00  4.86  3.57  0.0  \n",
       "2    2.52  3.14  0.25  1.22  4.32  1.21  0.00  4.49  4.72  0.0  \n",
       "3    3.65  3.20  0.22  0.82  3.60  1.52  0.00  7.27  3.63  0.0  \n",
       "4    2.25  2.81  0.20  2.52  5.18  1.52  0.00  6.55  3.38  0.0  \n",
       "..    ...   ...   ...   ...   ...   ...   ...   ...   ...  ...  \n",
       "741  3.20  3.66  1.01  0.37  1.40   NaN  1.32  1.22  0.76  0.0  \n",
       "742  3.76  2.06  1.10  0.69  1.44   NaN  1.15  1.25  0.75  0.0  \n",
       "743  3.82  2.66  1.26  0.73  1.53   NaN  1.08  1.28  0.76  0.0  \n",
       "744  3.03  4.89  1.09  0.60  1.72   NaN  2.26  1.25  0.75  0.0  \n",
       "745  3.79  2.53  1.17  0.70  1.47   NaN  2.26  1.22  0.76  0.0  \n",
       "\n",
       "[746 rows x 201 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('../技能实操-样卷1/data/data1.csv')\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "10de098f",
   "metadata": {},
   "outputs": [
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       "DATA_DATE    2015-01-02\n",
       "1                   2.5\n",
       "2                  0.65\n",
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       "4                  1.92\n",
       "                ...    \n",
       "196                1.29\n",
       "197                 0.0\n",
       "198                4.86\n",
       "199                3.57\n",
       "200                 0.0\n",
       "Name: 1, Length: 201, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7df054f",
   "metadata": {},
   "outputs": [],
   "source": []
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